Optical Entropy Network Mapped from Binary Feature Tree

The concept of a binary feature tree (BFT) and the principle of its formation are described. A pattern is divided into sub-parts by comparing its similarity with other patterns. The BFT is established by sub-parts of a group of patterns and mapped into a three layered neural network which Sethi called an entropy network. The interconnection pattern between the first and hidden layers is formed according to the “AND“ relationship of node feature patterns determined by BFT. The interconnection pattern between the hidden and last layers is obtained by training. The advantage of the proposed network is that the scale is small because a feature neuron is adopted and the interconnection is local instead of full; therefore, it is easily implemented by either hardware or software. Two simulation examples show the success of the entropy network for pattern recognition. A feature extraction by an optical inner product method is also described.Presented at 1996 International Topical Meeting on Optical Computing (OC ‘96), April 21-25, Sendai, Japan.